•Development and presentation of normative modeling approach based on hierarchical Bayesian modeling that can be applied to large multi-site neuroimaging data sets.•Comparison of performance of ...hierarchical Bayesian model including site as covariate to several common ways to harmonize for multi-site effects.•Presentation of normative modeling as site correction tool.
The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model.
•Average connectivity of valuation and control systems correlated with poly-drug use.•Correlations either independent or via two-way interactions with other substances.•Correlations reflect poly-drug ...use motivations (e.g. pleasure-seeking, pain relief).
Poly-drug consumption contributes to fatal overdose in more than half of all poly-drug users. Analyzing decision-making networks may give insight into the motivations behind poly-drug use. We correlated average functional connectivity of the valuation system (VS), executive control system (ECS) and valuation-control complex (VCC) in a large population sample (n = 992) with drug use behaviour. VS connectivity is correlated with sedative use, ECS connectivity is separately correlated with hallucinogens and opiates. Network connectivity is also correlated with drug use via two-way interactions with other substances including alcohol and tobacco. These preliminary findings can contribute to our understanding of the common combinations of substance co-use and associated neural patterns.
Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics ...of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r = 0.37, p<0.0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics.
•Hippocampal-cortical connectivity gradients change during naturalistic memory tasks.•Familiar cues accentuate the hippocampal anterior-posterior functional transition.•This transition is more ...posterior in the left hippocampus of subjects with MCI or AD.•Gradients of the anterior hippocampus map onto the default mode network.
The functional organization of the hippocampus mirrors that of the cortex, changing smoothly along connectivity gradients and abruptly at inter-areal boundaries. Hippocampal-dependent cognitive processes require flexible integration of these hippocampal gradients into functionally related cortical networks. To understand the cognitive relevance of this functional embedding, we acquired fMRI data while participants viewed brief news clips, either containing or lacking recently familiarized cues. Participants were 188 healthy mid-life adults and 31 adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We employed a recently developed technique - connectivity gradientography - to study gradually changing patterns of voxel to whole brain functional connectivity and their sudden transitions. We observed that functional connectivity gradients of the anterior hippocampus map onto connectivity gradients across the default mode network during these naturalistic stimuli. The presence of familiar cues in the news clips accentuates a stepwise transition across the boundary from the anterior to the posterior hippocampus. This functional transition is shifted in the posterior direction in the left hippocampus of individuals with MCI or AD. These findings shed new light on the functional integration of hippocampal connectivity gradients into large-scale cortical networks, how these adapt with memory context and how these change in the presence of neurodegenerative disease.
Normal brain function depends on a dynamic balance between local specialization and large-scale integration. It remains unclear, however, how local changes in functionally specialized areas can ...influence integrated activity across larger brain networks. By combining transcranial magnetic stimulation with resting-state functional magnetic resonance imaging, we tested for changes in large-scale integration following the application of excitatory or inhibitory stimulation on the human motor cortex. After local inhibitory stimulation, regions encompassing the sensorimotor module concurrently increased their internal integration and decreased their communication with other modules of the brain. There were no such changes in modular dynamics following excitatory stimulation of the same area of motor cortex nor were there changes in the configuration and interactions between core brain hubs after excitatory or inhibitory stimulation of the same area. These results suggest the existence of selective mechanisms that integrate local changes in neural activity, while preserving ongoing communication between brain hubs.
Hand preference is one of the behavioral expressions of lateralization in the brain. Previous fMRI studies showed the activation in several regions including the motor cortex and the cerebellum ...during single-hand movement. However, functional connectivity related to hand preference has not been investigated. Here, we used the generalized psychophysiological interaction (gPPI) approach to investigate the alteration of functional connectivity during single-hand movement from the resting state in right-hand subjects. The functional connectivity in interhemispheric motor-related regions including the supplementary motor area, the precentral gyrus, and the cerebellum was significantly increased during non-dominant hand movement, while functional connectivity was not increased during dominant hand movement. The general linear model (GLM) showed activation in contralateral supplementary motor area, contralateral precentral gyrus, and ipsilateral cerebellum during right- or left-hand movement. These results indicate that a combination of GLM and gPPI analysis can detect the lateralization of hand preference more clearly.
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•Interhemispheric functional connectivity increased during dominant hand movement•Functional connectivity was not changed during non-dominant hand movement•BOLD signal increase was almost symmetrical during right- or left-hand movement•Larger cluster size of the activated area was observed during dominant hand movement
Biological Sciences; Neuroscience; Techniques in neuroscience
One of the challenges of brain network analysis is to directly compare network organization between subjects, irrespective of the number or strength of connections. In this study, we used minimum ...spanning tree (MST; a unique, acyclic subnetwork with a fixed number of connections) analysis to characterize the human brain network to create an empirical reference network. Such a reference network could be used as a null model of connections that form the backbone structure of the human brain. We analyzed the MST in three diffusion‐weighted imaging datasets of healthy adults. The MST of the group mean connectivity matrix was used as the empirical null‐model. The MST of individual subjects matched this reference MST for a mean 58%–88% of connections, depending on the analysis pipeline. Hub nodes in the MST matched with previously reported locations of hub regions, including the so‐called rich club nodes (a subset of high‐degree, highly interconnected nodes). Although most brain network studies have focused primarily on cortical connections, cortical–subcortical connections were consistently present in the MST across subjects. Brain network efficiency was higher when these connections were included in the analysis, suggesting that these tracts may be utilized as the major neural communication routes. Finally, we confirmed that MST characteristics index the effects of brain aging. We conclude that the MST provides an elegant and straightforward approach to analyze structural brain networks, and to test network topological features of individual subjects in comparison to empirical null models.
The connectivity of the insula cortex is diverse. We present new models to characterize the resting-state connectional diversity of the human insula cortex and perform model selection using ...high-quality fMRI data from the Human Connectome Project. We first attempt to parcellate the insula into distinct subregions using traditional clustering methods, but find that the resulting subregions are not homogeneous and that the optimal number of subregions is substantially influenced by data smoothness. We then introduce the concept of a diversity curve, which we use to continuously parameterize the insula's Laplacian eigenmap with respect to streamlines propagated through the eigenmap's gradient field. To perform model selection, we compare the insula's diversity curve to benchmark diversity curves for: i) two distinct regions; ii) a continuum of gradual change; and, iii) an absence of any connectional diversity (i.e. homogenous region). Of the three benchmarks tested, we find that the insula's connectional diversity is most parsimoniously modeled as continuum of gradual change, from dorsal-posterior to ventral-anterior. We find that individuals who score high on measures of positive affect, self-efficacy, emotion recognition, motor dexterity and gustation show greater diversity within the anterior insula. Our findings are replicated using data from a second fMRI session. We conclude that the functional connectivity diversity of the insula can be characterized parsimoniously as a continuum, avoiding the vexed task of determining an optimal number of insula subregions, and that inter-individual variation in this continuum can explain significant variation in behavior.
•Insula subregions mapped with clustering are not homogeneous and depend on smoothing.•No evidence for discrete transitions in fMRI connectivity across insula’s topography.•Functional connectivity diversity of insula parsimoniously modeled as a continuum.•Inter-individual variation in the continuum explains variation in behavioral traits.
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